scholarly journals Single-Valued Neutrosophic Set Correlation Coefficient and Its Application in Fault Diagnosis

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1371
Author(s):  
Shchur Iryna ◽  
Yu Zhong ◽  
Wen Jiang ◽  
Xinyang Deng ◽  
Jie Geng
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Triangular Fuzzy Number ◽  
Neutrosophic Set ◽  
Mechanical Equipment ◽  
Inaccurate Information ◽  
The Given ◽  
Set Correlation ◽  
Accuracy Degree ◽  
Weighted Correlation

With the increasing automation of mechanical equipment, fault diagnosis becomes more and more important. However, the factors that cause mechanical failures are becoming more and more complex, and the uncertainty and coupling between the factors are getting higher and higher. In order to solve the given problem, this paper proposes a single-valued neutrosophic set ISVNS algorithm for processing of uncertain and inaccurate information in fault diagnosis, which generates neutrosophic set by triangular fuzzy number and introduces the formula of the improved weighted correlation coefficient. Since both the single-valued neutrosophic set data and the ideal neutrosophic set data are considered, the proposed method solves the fault diagnosis problem more effectively. Finally, experiments show that the algorithm can significantly improve the accuracy degree of fault diagnosis, and can better satisfy the diagnostic requirements in practice.

scholarly journals The Fourth Axiom of Similarity Measures

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1735 ◽  
Author(s):  
Chun-Hsiao Chu ◽  
Chih-Ping Yen ◽  
Yi-Fong Lin
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Similarity Measure ◽  
Intuitionistic Fuzzy Set ◽  
Similarity Measures ◽  
Turbine Engine ◽  
Symmetric Structure ◽  
Source Paper ◽  
Weighted Correlation

In this research, the fourth axiom to improve the well-defined examination of similarity measures is studied, where the measures have a symmetric structure. We first provide a theoretic enhancement of three correlation coefficient similarity measures that were proposed by a source paper. Second, we use the same numerical example proposed by the source paper for pattern recognition problems to illustrate that the weighted correlation coefficient similarity measure is dependent on the set of weights. Finally, we demonstrate that the correlation coefficient similarity measure in the intuitionistic fuzzy set environment can address the issue of practical fault diagnosis when solving the turbine engine problems using similarity measures with symmetric characteristics.

scholarly journals Wayside Bearing Fault Diagnosis Based on Envelope Analysis Paved with Time-Domain Interpolation Resampling and Weighted-Correlation-Coefficient-Guided Stochastic Resonance

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Yongbin Liu ◽  
Qiang Qian ◽  
Fang Liu ◽  
Siliang Lu ◽  
Qingbo He ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Stochastic Resonance ◽  
Time Domain ◽  
Spectrum Analysis ◽  
Doppler Effect ◽  
Railway Vehicle ◽  
Bearing Fault ◽  
Envelope Spectrum ◽  
Weighted Correlation

Envelope spectrum analysis is a simple, effective, and classic method for bearing fault identification. However, in the wayside acoustic health monitoring system, owing to the high relative moving speed between the railway vehicle and the wayside mounted microphone, the recorded signal is embedded with Doppler effect, which brings in shift and expansion of the bearing fault characteristic frequency (FCF). What is more, the background noise is relatively heavy, which makes it difficult to identify the FCF. To solve the two problems, this study introduces solutions for the wayside acoustic fault diagnosis of train bearing based on Doppler effect reduction using the improved time-domain interpolation resampling (TIR) method and diagnosis-relevant information enhancement using Weighted-Correlation-Coefficient-Guided Stochastic Resonance (WCCSR) method. First, the traditional TIR method is improved by incorporating the original method with kinematic parameter estimation based on time-frequency analysis and curve fitting. Based on the estimated parameters, the Doppler effect is removed using the TIR easily. Second, WCCSR is employed to enhance the diagnosis-relevant period signal component in the obtained Doppler-free signal. Finally, paved with the above two procedures, the local fault is identified using envelope spectrum analysis. Simulated and experimental cases have verified the effectiveness of the proposed method.

scholarly journals A Novel Domain Adaptation-Based Intelligent Fault Diagnosis Model to Handle Sample Class Imbalanced Problem

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3382
Author(s):  
Zhongwei Zhang ◽  
Mingyu Shao ◽  
Liping Wang ◽  
Sujuan Shao ◽  
Chicheng Ma
Keyword(s):  
Fault Diagnosis ◽  
Domain Adaptation ◽  
Class Imbalance ◽  
Rolling Bearing ◽  
Industrial Applications ◽  
Mechanical Equipment ◽  
Reliable Operation ◽  
Cross Domain ◽  
Geometric Difference ◽  
Model Generalization

As the key component to transmit power and torque, the fault diagnosis of rotating machinery is crucial to guarantee the reliable operation of mechanical equipment. Regrettably, sample class imbalance is a common phenomenon in industrial applications, which causes large cross-domain distribution discrepancies for domain adaptation (DA) and results in performance degradation for most of the existing mechanical fault diagnosis approaches. To address this issue, a novel DA approach that simultaneously reduces the cross-domain distribution difference and the geometric difference is proposed, which is defined as MRMI. This work contains three parts to improve the sample class imbalance issue: (1) A novel distance metric method (MVD) is proposed and applied to improve the performance of marginal distribution adaptation. (2) Manifold regularization is combined with instance reweighting to simultaneously explore the intrinsic manifold structure and remove irrelevant source-domain samples adaptively. (3) The ℓ2-norm regularization is applied as the data preprocessing tool to improve the model generalization performance. The gear and rolling bearing datasets with class imbalanced samples are applied to validate the reliability of MRMI. According to the fault diagnosis results, MRMI can significantly outperform competitive approaches under the condition of sample class imbalance.

scholarly journals A Fusion Feature Extraction Method Using EEMD and Correlation Coefficient Analysis for Bearing Fault Diagnosis

2018 ◽  
Vol 8 (9) ◽  
pp. 1621 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Yong Ren ◽  
Gongbo Zhou ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Feature Space ◽  
Support Vector ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Acceleration Sensors ◽  
Reconstructed Signal ◽  
Original Feature

Acceleration sensors are frequently applied to collect vibration signals for bearing fault diagnosis. To fully use these vibration signals of multi-sensors, this paper proposes a new approach to fuse multi-sensor information for bearing fault diagnosis by using ensemble empirical mode decomposition (EEMD), correlation coefficient analysis, and support vector machine (SVM). First, EEMD is applied to decompose the vibration signal into a set of intrinsic mode functions (IMFs), and a correlation coefficient ratio factor (CCRF) is defined to select sensitive IMFs to reconstruct new vibration signals for further feature fusion analysis. Second, an original feature space is constructed from the reconstructed signal. Afterwards, weights are assigned by correlation coefficients among the vibration signals of the considered multi-sensors, and the so-called fused features are extracted by the obtained weights and original feature space. Finally, a trained SVM is employed as the classifier for bearing fault diagnosis. The diagnosis results of the original vibration signals, the first IMF, the proposed reconstruction signal, and the proposed method are 73.33%, 74.17%, 95.83% and 100%, respectively. Therefore, the experiments show that the proposed method has the highest diagnostic accuracy, and it can be regarded as a new way to improve diagnosis results for bearings.

scholarly journals Correlation Coefficient Based Automatic Optic Disc Detection from Retinal Images

2020 ◽  
Author(s):  
S. Anand ◽  
R. Prabhadevi ◽  
D. Rini
Keyword(s):  
Optic Disc ◽  
Correlation Coefficient ◽  
Retinal Image ◽  
Retinal Images ◽  
Detection Accuracy ◽  
Maximum Correlation ◽  
Exact Location ◽  
Peak Intensity ◽  
The Given

ABSTRACTIn this paper an algorithm to detect the optic disc (OD) automatically is described. The proposed method is based on the circular brightness of the OD and its correlation coefficient. At first the peak intensity points are taken, a mask is generated for the given image which gives the circular bright regions of the image. To locate the OD accurately, a pattern is generated which is similar to the OD. By correlating the retinal image with the pattern generated, the maximum correlation of the pattern with the OD is obtained. On locating the coordinates of maximum correlation, the exact location of the OD is detected. The proposed algorithm has been tested with DRIVE database images and an average OD detection accuracy of 95% was obtained for healthy and pathological retinas respectively.

scholarly journals A New Weighted Correlation Coefficient Method to Evaluate Reconstructed Brain Electrical Sources

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Jong-Ho Choi ◽  
Min-Hyuk Kim ◽  
Luan Feng ◽  
Chany Lee ◽  
Hyun-Kyo Jung
Keyword(s):  
Correlation Coefficient ◽  
Method Comparison ◽  
Evaluation Metrics ◽  
Cortical Surface ◽  
Cortical Sources ◽  
Surface Accuracy ◽  
Simulation Results ◽  
Electrical Activities ◽  
Coefficient Method ◽  
Weighted Correlation

Various inverse algorithms have been proposed to estimate brain electrical activities with magnetoencephalography (MEG) and electroencephalography (EEG). To validate and compare the performances of inverse algorithms, many researchers have used artificially constructed EEG and MEG datasets. When the artificial sources are reconstructed on the cortical surface, accuracy of the source estimates has been difficult to evaluate. In this paper, we suggest a new measure to evaluate the reconstructed EEG/MEG cortical sources more accurately. To validate the usefulness of the proposed method, comparison between conventional and proposed evaluation metrics was conducted using artificial cortical sources simulated under different noise conditions. The simulation results demonstrated that only the proposed method could reflect the source space geometry regardless of the number of source peaks.

scholarly journals Bearing Fault Diagnosis Based on Improved Convolutional Deep Belief Network

2020 ◽  
Vol 10 (18) ◽  
pp. 6359 ◽  
Author(s):  
Shuangjie Liu ◽  
Jiaqi Xie ◽  
Changqing Shen ◽  
Xiaofeng Shang ◽  
Dong Wang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Vibration Signal ◽  
Deep Belief Network ◽  
Mechanical Equipment ◽  
Belief Network ◽  
Bearing Faults ◽  
Qualitative And Quantitative ◽  
Proposed Model ◽  
Different Types

Mechanical equipment fault detection is critical in industrial applications. Based on vibration signal processing and analysis, the traditional fault diagnosis method relies on rich professional knowledge and artificial experience. Achieving accurate feature extraction and fault diagnosis is difficult using such an approach. To learn the characteristics of features from data automatically, a deep learning method is used. A qualitative and quantitative method for rolling bearing faults diagnosis based on an improved convolutional deep belief network (CDBN) is proposed in this study. First, the original vibration signal is converted to the frequency signal with the fast Fourier transform to improve shallow inputs. Second, the Adam optimizer is introduced to accelerate model training and convergence speed. Finally, the model structure is optimized. A multi-layer feature fusion learning structure is put forward wherein the characterization capabilities of each layer can be fully used to improve the generalization ability of the model. In the experimental verification, a laboratory self-made bearing vibration signal dataset was used. The dataset included healthy bearings, nine single faults of different types and sizes, and three different types of composite fault signals. The results of load 0 kN and 1 kN both indicate that the proposed model has better diagnostic accuracy, with an average of 98.15% and 96.15%, compared with the traditional stacked autoencoder, artificial neural network, deep belief network, and standard CDBN. With improved diagnostic accuracy, the proposed model realizes reliable and effective qualitative and quantitative diagnosis of bearing faults.

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